LLM GPU requirements

Qwen3 32B GPU requirements: VRAM, GPUs and servers

How much GPU memory Qwen3 32B needs in BF16, FP8 and 4-bit, how its KV cache grows with context, what fine-tuning takes, and which servers run it, with monthly prices.

Parameters: 32.8B
Architecture: Dense 64 layers
KV heads: 8 × 128 dim
Context: 32k tokens

Open in the VRAM calculator

Cheapest servers that run it

FP8 weights · 8k context · 8 concurrent requests · about 54 GB

NVIDIA RTX PRO 6000 Blackwell In stock

96 GB GDDR7 · Single GPU

$1,390/mo
NVIDIA H100 SXM In stock

80 GB HBM3 · Single GPU

$2,190/mo
NVIDIA H200 In stock

141 GB HBM3e · Single GPU

$3,090/mo

Delivered in under 10 minutes after payment.

Inference memory

How much VRAM Qwen3 32B needs

Weights take parameters × bytes per parameter. Serving adds the KV cache and about 10% runtime overhead; the totals below assume an 8k context with 8 concurrent requests and a BF16 KV cache.

Qwen3 32B memory by precision and the cheapest server that fits
Precision	Weights	Total to serve	Cheapest server that fits
BF16 2 bytes per parameter	66 GB	90 GB	NVIDIA H200 $3,090/mo
FP8 1 byte per parameter	33 GB	54 GB	NVIDIA RTX PRO 6000 Blackwell $1,390/mo
4-bit 0.5 bytes per parameter	16 GB	36 GB	NVIDIA L40S $890/mo

GGUF quantizations for llama.cpp and Ollama

Qwen3 32B GGUF file sizes and the smallest GPU for one 8k request
Quantization	Weights	Smallest GPU, one 8k request
Q8_0 about 8.5 bits per weight	35 GB	NVIDIA L40S 48 GB · $890/mo
Q6_K about 6.56 bits per weight	27 GB	NVIDIA L40S 48 GB · $890/mo
Q5_K_M about 5.69 bits per weight	23 GB	NVIDIA GeForce RTX 5090 32 GB · $449/mo
Q4_K_M about 4.85 bits per weight	20 GB	NVIDIA GeForce RTX 5090 32 GB · $449/mo

Estimates with about 8% of GPU memory kept free. Quantized weights also keep some layers in higher precision, and frameworks differ in overhead; check the free memory your engine reports at startup. A model split across GPUs with tensor parallelism runs best on NVLink servers (HGX); the 8× RTX PRO 6000 server links its GPUs over PCIe.

GPUs and servers

Which GPUs can run Qwen3 32B?

Yes. In FP8, a single NVIDIA H100 SXM (80 GB, $2,190 a month at OverGPU) runs Qwen3 32B with an 8k context and 8 concurrent requests; one NVIDIA L40S (48 GB) holds a single 8k request. The table checks every configuration we rent in each precision.

Whether each GPU configuration runs Qwen3 32B in BF16, FP8 and 4-bit
Configuration	BF16	FP8	4-bit
NVIDIA GeForce RTX 5090 32 GB · $449/mo	—	—	1 req
NVIDIA L40S 48 GB · $890/mo	—	1 req	Yes
NVIDIA H100 SXM 80 GB · $2,190/mo	—	Yes	Yes
NVIDIA RTX PRO 6000 Blackwell 96 GB · $1,390/mo	1 req	Yes	Yes
NVIDIA H200 141 GB · $3,090/mo	Yes	Yes	Yes
NVIDIA B200 180 GB · $4,590/mo	Yes	Yes	Yes
NVIDIA B300 288 GB · $5,190/mo	Yes	Yes	Yes
8× NVIDIA HGX H100 8 × 80 GB · $16,900/mo	Yes	Yes	Yes
8× NVIDIA RTX PRO 6000 Blackwell 8 × 96 GB · $10,490/mo	Yes	Yes	Yes
8× NVIDIA HGX H200 8 × 141 GB · $23,900/mo	Yes	Yes	Yes
8× NVIDIA HGX B200 8 × 180 GB · $34,900/mo	Yes	Yes	Yes
8× NVIDIA HGX B300 8 × 288 GB · $39,900/mo	Yes	Yes	Yes
8× AMD Instinct MI355X 8 × 288 GB · $19,900/mo	Yes	Yes	Yes

Yes: 8 concurrent requests with an 8k context each. 1 req: a single 8k request at a time. —: the weights, KV cache and runtime do not fit. Same formulas as the tables above, with about 8% of GPU memory kept free.

KV cache

Qwen3 32B KV cache by context length

Each token in flight stores keys and values for every layer: 2 × 64 layers × 8 KV heads × 128 dimensions × 2 bytes = 256 KB per token in BF16. An FP8 KV cache halves these figures.

KV cache size by context length and number of concurrent requests
Context per request	1 request	8 requests	32 requests
4k tokens	1.1 GB	8.6 GB	34 GB
32k tokens	8.6 GB	69 GB	275 GB
128k tokens	34 GB	275 GB	1,100 GB

Fine-tuning

Memory to fine-tune Qwen3 32B

Rough totals before tuning batch size and sequence length: QLoRA keeps 4-bit base weights, LoRA keeps BF16 weights frozen, and a full fine-tune with AdamW needs about 16 bytes per parameter plus activations, sharded across GPUs.

Fine-tuning memory by method and the cheapest server that fits
Method	Approximate memory	Cheapest server that fits
QLoRA (4-bit base)	25 GB	NVIDIA GeForce RTX 5090 $449/mo
LoRA (BF16 base)	81 GB	NVIDIA RTX PRO 6000 Blackwell $1,390/mo
Full fine-tune (AdamW, mixed precision)	630 GB	8× NVIDIA RTX PRO 6000 Blackwell $10,490/mo

Full fine-tuning assumes sharded training (FSDP or ZeRO-3) with activation checkpointing. See the fine-tuning guide for methods and settings.

About Qwen3 32B

Qwen3 32B is the largest dense model of the Qwen3 family: 64 layers, 8 KV heads and Apache 2.0 weights. With 64 layers against 40 for Qwen3 14B, its KV cache per token is 60% larger, so long contexts and high concurrency weigh heavily in your sizing.

Every figure on this page comes from the model’s published architecture and the same formulas as our VRAM calculator, where you can change the context length, concurrency and KV cache precision.

Model facts

Developer: Alibaba Qwen
License: Apache 2.0
Type: Dense
Parameters: 32.8B
Layers: 64
Attention: 64 query heads, 8 KV headsgrouped-query attention, head dimension 128
Context window: 32k tokens native, about 128k with YaRN

FAQ

Qwen3 32B GPU FAQ

The short version. The help center has the details, and customers with an active server get answers by ticket, 24/7.

Visit the help center

How much VRAM does Qwen3 32B need?

About 66 GB for the weights in BF16, 33 GB in FP8 and 16 GB in 4-bit, plus the KV cache: about 262 MB per 1,000 tokens in flight with a BF16 cache. Serving it in FP8 with an 8k context and 8 concurrent requests takes about 54 GB in total.

Can Qwen3 32B run on a single GPU?

Yes. In FP8, a single NVIDIA RTX PRO 6000 Blackwell (96 GB) serves it with an 8k context and 8 concurrent requests. Longer contexts or more users need more memory for the KV cache, so check your case in the VRAM calculator.

What is the cheapest OverGPU server for Qwen3 32B?

For FP8 serving with an 8k context and 8 concurrent requests, the NVIDIA RTX PRO 6000 Blackwell at $1,390 per month is the lowest-priced configuration that fits. For other contexts and concurrency, size it in the VRAM calculator.

How much memory do I need to fine-tune Qwen3 32B?

Roughly 25 GB with QLoRA, 81 GB with LoRA on BF16 weights, and 630 GB for a full fine-tune with AdamW in mixed precision, before tuning batch size and sequence length. Full fine-tunes of large models are sharded across GPUs with FSDP or ZeRO-3.

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